"""simple docstring"""







import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class 			UpperCamelCase						(     _lowerCAmelCase		, _lowerCAmelCase		, unittest.TestCase    ):
   lowercase				=    VQModel
   lowercase				=    '''sample'''
   @property
   def      _UpperCAmelCase  (     self	,__UpperCamelCase=(32, 32)     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							List[str]						=      4
        lowercase_      :							Union[str, Any]						=      3

        lowercase_      :							Union[str, Any]						=      floats_tensor((batch_size, num_channels) + sizes     ).to(UpperCAmelCase_     )

        return {"sample": image}
   @property
   def      _UpperCAmelCase  (     self     )      -> List[Any]:


        '''simple docstring'''


        return (3, 32, 32)
   @property
   def      _UpperCAmelCase  (     self     )      -> Tuple:


        '''simple docstring'''


        return (3, 32, 32)
   def      _UpperCAmelCase  (     self     )      -> Optional[int]:


        '''simple docstring'''


        lowercase_      :							str						=      {
            'block_out_channels': [32, 64],
            'in_channels': 3,
            'out_channels': 3,
            'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
            'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
            'latent_channels': 3,
        }
        lowercase_      :							Any						=      self.dummy_input
        return init_dict, inputs_dict
   def      _UpperCAmelCase  (     self     )      -> List[Any]:


        '''simple docstring'''


        pass
   def      _UpperCAmelCase  (     self     )      -> str:


        '''simple docstring'''


        pass
   def      _UpperCAmelCase  (     self     )      -> List[Any]:


        '''simple docstring'''


        lowercase_				,    lowercase_      :							Optional[Any]						=      VQModel.from_pretrained('fusing/vqgan-dummy'	,output_loading_info=UpperCAmelCase_     )
        self.assertIsNotNone(UpperCAmelCase_     )
        self.assertEqual(len(loading_info['missing_keys']     )	,0     )

        model.to(UpperCAmelCase_     )
        lowercase_      :							str						=      model(**self.dummy_input     )

        assert image is not None, "Make sure output is not None"







   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							List[str]						=      VQModel.from_pretrained('fusing/vqgan-dummy'     )
        model.to(UpperCAmelCase_     ).eval()

        torch.manual_seed(0     )
        if torch.cuda.is_available():
             torch.cuda.manual_seed_all(0     )

        lowercase_      :							Optional[Any]						=      torch.randn(1	,model.config.in_channels	,model.config.sample_size	,model.config.sample_size     )
        lowercase_      :							Dict						=      image.to(UpperCAmelCase_     )
        with torch.no_grad():
             lowercase_      :							str						=      model(UpperCAmelCase_     ).sample

        lowercase_      :							Optional[Any]						=      output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        lowercase_      :							Dict						=      torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]     )
        # fmt: on
        self.assertTrue(torch.allclose(UpperCAmelCase_	,UpperCAmelCase_	,atol=1e-3     )     )



"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets



class 	__a	(   _lowerCAmelCase						):






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							,   "prajjwal1/bert-tiny"      )
              UpperCamelCase       			=		BertTokenizer.from_pretrained("bert-base-uncased"      )

              UpperCamelCase       			=		bertabert.config.encoder.vocab_size
              UpperCamelCase       			=		tokenizer.sep_token_id
              UpperCamelCase       			=		tokenizer.cls_token_id
              UpperCamelCase       			=		128

              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="train[:1%]"      )
              UpperCamelCase       			=		datasets.load_dataset("cnn_dailymail"							,   "3.0.0"							,   split="validation[:1%]"      )

              UpperCamelCase       			=		train_dataset.select(range(32      )      )
              UpperCamelCase       			=		val_dataset.select(range(16      )      )

              UpperCamelCase       			=		4

              def _map_to_encoder_decoder_inputs(UpperCAmelCase_			:				Any      ):
                     # Tokenizer will automatically set [BOS] <text> [EOS]
                     UpperCamelCase       			=		tokenizer(batch["article"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=512      )
                     UpperCamelCase       			=		tokenizer(batch["highlights"]							,   padding="max_length"							,   truncation=UpperCAmelCase_							,   max_length=128      )
                     UpperCamelCase       			=		inputs.input_ids
                     UpperCamelCase       			=		inputs.attention_mask

                     UpperCamelCase       			=		outputs.input_ids
                     UpperCamelCase       			=		outputs.input_ids.copy()
                     UpperCamelCase       			=		[
                         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
                     ]
                     UpperCamelCase       			=		outputs.attention_mask

                     assert all(len(UpperCAmelCase_      ) == 512 for x in inputs.input_ids      )
                     assert all(len(UpperCAmelCase_      ) == 128 for x in outputs.input_ids      )

                     return batch

              def _compute_metrics(UpperCAmelCase_			:				Any      ):
                     UpperCamelCase       			=		pred.label_ids
                     UpperCamelCase       			=		pred.predictions

                     # all unnecessary tokens are removed
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )

                     UpperCamelCase       			=		sum([int(pred_str[i] == label_str[i]      ) for i in range(len(UpperCAmelCase_      )      )]      ) / len(UpperCAmelCase_      )

                     return {"accuracy": accuracy}

              # map train dataset
              UpperCamelCase       			=		train_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              train_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              # same for validation dataset
              UpperCamelCase       			=		val_dataset.map(
                  _map_to_encoder_decoder_inputs							,   batched=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   remove_columns=["article", "highlights"]							,   )
              val_dataset.set_format(
                  type="torch"							,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							,   )

              UpperCamelCase       			=		self.get_auto_remove_tmp_dir()

              UpperCamelCase       			=		SeqaSeqTrainingArguments(
                  output_dir=UpperCAmelCase_							,   per_device_train_batch_size=UpperCAmelCase_							,   per_device_eval_batch_size=UpperCAmelCase_							,   predict_with_generate=UpperCAmelCase_							,   evaluation_strategy="steps"							,   do_train=UpperCAmelCase_							,   do_eval=UpperCAmelCase_							,   warmup_steps=0							,   eval_steps=2							,   logging_steps=2							,   )

              # instantiate trainer
              UpperCamelCase       			=		SeqaSeqTrainer(
                  model=UpperCAmelCase_							,   args=UpperCAmelCase_							,   compute_metrics=_compute_metrics							,   train_dataset=UpperCAmelCase_							,   eval_dataset=UpperCAmelCase_							,   tokenizer=UpperCAmelCase_							,   )

              # start training
              trainer.train()





